TY - GEN
T1 - Advancing 3D Mesh Analysis
T2 - 27th International Conference on Pattern Recognition, ICPR 2024
AU - Böhm, Stefan Andreas
AU - Neumayer, Martin
AU - Zagar, Bare Luka
AU - Riß, Fabian
AU - Kortüm, Christian
AU - Knoll, Alois
N1 - Publisher Copyright:
© The Author(s) 2025.
PY - 2025
Y1 - 2025
N2 - Driven by increasing customer demands, manufacturing processes now encompass increasingly intricate workflows. The industry uses computer-aided process planning to manage these complex manufacturing processes effectively. A crucial task here is to analyze product data and determine the required machining features, represented as 3D mesh geometries. However, a notable challenge arises, particularly with custom products, where the interpretation of the 3D mesh geometry varies significantly depending on the available machinery and expert preferences. This study introduces a configurable automated feature recognition framework based on expert knowledge. Experts can use a configurable synthetic data generator to encode their requirements within this framework via the training data. A machine-learning graph classification approach is used to recognize the 3D geometries of machining features in the generated data, based on to the user requirements. The system accomplishes this without requiring for data conversion into alternative formats, such as voxel or pixel representations, like other approaches are forced to.
AB - Driven by increasing customer demands, manufacturing processes now encompass increasingly intricate workflows. The industry uses computer-aided process planning to manage these complex manufacturing processes effectively. A crucial task here is to analyze product data and determine the required machining features, represented as 3D mesh geometries. However, a notable challenge arises, particularly with custom products, where the interpretation of the 3D mesh geometry varies significantly depending on the available machinery and expert preferences. This study introduces a configurable automated feature recognition framework based on expert knowledge. Experts can use a configurable synthetic data generator to encode their requirements within this framework via the training data. A machine-learning graph classification approach is used to recognize the 3D geometries of machining features in the generated data, based on to the user requirements. The system accomplishes this without requiring for data conversion into alternative formats, such as voxel or pixel representations, like other approaches are forced to.
KW - Graph Classification
KW - Graph Neural Networks
KW - Intersecting 3D Meshes
UR - http://www.scopus.com/inward/record.url?scp=85211951449&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-78166-7_10
DO - 10.1007/978-3-031-78166-7_10
M3 - Conference contribution
AN - SCOPUS:85211951449
SN - 9783031781650
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 143
EP - 159
BT - Pattern Recognition - 27th International Conference, ICPR 2024, Proceedings
A2 - Antonacopoulos, Apostolos
A2 - Chaudhuri, Subhasis
A2 - Chellappa, Rama
A2 - Liu, Cheng-Lin
A2 - Bhattacharya, Saumik
A2 - Pal, Umapada
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 1 December 2024 through 5 December 2024
ER -